In credit card transactions, a major security problem exists whenever credit card information is transmitted over the Internet or telephone lines. In addition, because of the frequency with which credit cards, passports, and other personal documents, are lost and stolen, there exists a need to correctly, quickly and reliably verify the identity of the bearers of these documents.
In a typical credit card transaction, as seen in FIG. 1, a merchant 10 transmits a credit card number, the expiry date and a purchase order over the Internet or telephone lines 12 to a verification agent 14. The agent 14 receiving this information accesses the cardholder's credit information and after comparing the latter to the purchase order amount, either accepts or rejects the transaction. If the transaction is accepted, an approval, code is transmitted back 16 to the merchant 10 via telephone line 12. Even if the transaction is accepted there is a risk that the card is stolen and is being used fraudulently. Accordingly, there is a need to be able to quickly, accurately and securely identify the bearer of the card.
Biometrics can be used to accurately verify identity, however, biometric information sent over the Internet or telephone lines can still be intercepted and subsequently utilized for fraudulent transactions.
Various approaches have been developed to identify persons by biometrics, including unique gestures such as speech and handwriting. Such speech and handwriting recognition systems perform recognition of something that moves, leaving a “trajectory” in space and time. Typical speech recognition systems match transformed speech against a stored representation. Most speech recognition systems use some form of spectral representation, such as spectral templates or Hidden Markov Models (HMMs).
Handwriting can be analyzed in real time or after it has been formed. “Real time” or dynamic recognition systems identify handwriting as a user writes, identifying such things as number of strokes, the ordering of strokes and the direction and velocity profile of each stroke. “Real time” systems are also interactive, allowing users to correct recognition errors, adapt to the system, or see the immediate results of an editing command. Most on-line tablets capture writing as a sequence of coordinate points.
Handwriting recognition is complicated in part, because there are many different ways of generating the same character. For example, the four lines of the letter E can be drawn in any order. Handwriting tablets must also take into account character blending and merging, which is similar to the continuous speech problem. In other words, blending and merging make it difficult for a recognition system to determine where one character ends and the next one begins (or in the case of speech recognition systems, where one word ends and the next one begins). In addition, different characters can look quite similar and are, therefore, difficult to distinguish. Thus, prior to performing the character recognition, handwriting tablets pre-process the characters. Preprocessing typically involves properly spacing the characters and filtering out noise from the tablet. The more complicated processing occurs during actual character recognition.
Some character recognition processes, using binary decision trees, prune possible characters by identifying features. Normally simple features are identified first, such as searching for the dots above the letters “i” and “j”. Features based on both static and dynamic features can be used for character recognition. Other character recognition processes involve the creation of zones, which define the directions a pen point can travel (usually eight), and define each character in terms of a set of zones. Look-up tables or dictionaries can be used to classify or identify the characters based on their features or sets of zones.
Another character recognition scheme relies on signal processing, in which curves from unknown forms are matched against prototype characters. They are matched as functions of time or as Fourier coefficients To reduce errors, elastic matching schemes (stretching and bending drawn curves) may be used. However, these methods are computationally intensive and, therefore, tend to be slow and expensive.
Most handwriting examination tablets reveal that recognition of dynamic features of characters is possible, as in speech. However, for the reasons discussed above, it is easier to recognize isolated characters than strings of characters. In most systems, the lag-time in recognition is typically about a second, and recognition rates are not very high. Reported rates of 95% are achieved only for very carefully formed writing.
Most handwriting examination tablets reveal that recognition of dynamic features of characters is possible, as in speech. However, for the reasons discussed above, it is easier to recognize isolated characters than strings of characters. Most systems lag recognition by about a second, and recognition rates are not very high. Reported rates of 95% are achieved only for very carefully formed writing.
For each of the types of recognition systems discussed above, a sample input (i.e. a voice or signature sample) must be processed and compared with a stored reference gesture in order to verify the identity of the subject. Normally, the reference gestures are located on a remote server and accessed by telephone lines or the Internet. The sample input must be sent to the remote server where it is compared to the reference gesture. Such a procedure is obviously exposed to the risk of security breaches. Furthermore, there is a cost associated with the maintenance of a remote server, and processing is delayed by the need to access the server. Accordingly, it is an object of the present invention to provide a quick and secure on-site method of identification, which is accurate and cost effective.